Bitter taste is an unpleasant taste modality that affects food consumption. Bitter peptides are generated during enzymatic processes that produce functional, bioactive protein hydrolysates or during the aging process of fermented products such as cheese, soybean protein, and wine. Understanding the underlying peptide sequences responsible for bitter taste can pave the way for more efficient identification of these peptides. This paper presents BitterPep-GCN, a feature-agnostic graph convolution network for bitter peptide prediction. The graph-based model learns the embedding of amino acids in the bitter peptide sequences and uses mixed pooling for bitter classification. BitterPep-GCN was benchmarked using BTP640, a publicly available bitter peptide dataset. The latent peptide embeddings generated by the trained model were used to analyze the activity of sequence motifs responsible for the bitter taste of the peptides. Particularly, we calculated the activity for individual amino acids and dipeptide, tripeptide, and tetrapeptide sequence motifs present in the peptides. Our analyses pinpoint specific amino acids, such as F, G, P, and R, as well as sequence motifs, notably tripeptide and tetrapeptide motifs containing FF, as key bitter signatures in peptides. This work not only provides a new predictor of bitter taste for a more efficient identification of bitter peptides in various food products but also gives a hint into the molecular basis of bitterness. Scientific Contribution Our work provides the first application of Graph Neural Networks for the prediction of peptide bitter taste. The best-developed model, BitterPep-GCN, learns the embedding of amino acids in the bitter peptide sequences and uses mixed pooling for bitter classification. The embeddings were used to analyze the sequence motifs responsible for the bitter taste.

中文翻译：

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使用图神经网络预测苦肽


苦味是一种影响食物消费的令人不快的味道方式。苦肽是在产生功能性生物活性蛋白质水解物的酶促过程中或在奶酪、大豆蛋白和葡萄酒等发酵产品的陈酿过程中产生的。了解导致苦味的潜在肽序列可以为更有效地鉴定这些肽铺平道路。本文介绍了 BitterPep-GCN，这是一种用于苦肽预测的与特征无关的图卷积网络。基于图的模型学习氨基酸在苦味肽序列中的嵌入，并使用混合进行苦味分类。BitterPep-GCN 使用 BTP640（一个公开的苦肽数据集）进行基准测试。由训练模型生成的潜在肽嵌入用于分析导致肽苦味的序列基序的活性。特别是，我们计算了肽中存在的单个氨基酸和二肽、三肽和四肽序列基序的活性。我们的分析确定了特定的氨基酸，如 F、G、P 和 R，以及序列基序，特别是含有 FF 的三肽和四肽基序，作为肽中的关键苦味特征。这项工作不仅为更有效地鉴定各种食品中的苦味肽提供了新的苦味预测因子，而且还为苦味的分子基础提供了线索。科学贡献我们的工作首次将图神经网络用于预测肽苦味。开发最好的模型 BitterPep-GCN 学习氨基酸在苦味肽序列中的嵌入，并使用混合混合混合进行苦味分类。 嵌入用于分析导致苦味的序列基序。